东北大学学报(自然科学版) ›› 2025, Vol. 46 ›› Issue (3): 1-11.DOI: 10.12068/j.issn.1005-3026.2025.20239039

• 信息与控制 •    下一篇

冰雪路面条件下多场景自动驾驶车辆主动避障路径规划

裴玉龙1, 翟双柱2   

  1. 1.东北林业大学 土木与交通学院,黑龙江 哈尔滨 150040
    2.东北林业大学 理学院,黑龙江 哈尔滨 150040
  • 收稿日期:2023-08-02 出版日期:2025-03-15 发布日期:2025-05-29
  • 作者简介:裴玉龙(1961—),男,黑龙江桦川人,东北林业大学教授,博士生导师.

Active Obstacle Avoidance Path Planning for Multi-scenario Autonomous Vehicles Under Icy and Snowy Road Conditions

Yu-long PEI1, Shuang-zhu ZHAI2   

  1. 1.School of Civil Engineering and Transportation,Northeast Forestry University,Harbin 150040,China
    2.School of Sciences,Northeast Forestry University,Harbin 150040,China. Corresponding author: ZHAI Shuang-zhu,E-mail: 401481643@qq. com
  • Received:2023-08-02 Online:2025-03-15 Published:2025-05-29

摘要:

针对自动驾驶车辆在冰雪路面易失稳的问题,提出改进的快速扩展随机树(RRT)路径规划算法.首先,建立冰雪路面车辆动力学模型,引入路面附着系数;然后,采用结合车头指向及转向角的全局目标偏向性采样,结合避撞检测与速度-附着系数下的最大曲率约束,改善传统RRT算法问题;最后,使用双五次多项式平滑路径,满足稳定性、制动器约束及舒适性.通过MATLAB-Simulink与CarSim联合仿真,比较改进RRT算法与传统算法在多场景条件下的性能.实验表明,改进RRT算法显著提升路径平滑度,降低曲率突变,用时短、成功率高,且在冰雪路面行驶时稳定性良好.

关键词: 自动驾驶车辆, 路径规划, 快速扩展随机树, 冰雪路面, 多场景

Abstract:

Addressing the issue of autonomous vehicles’ instability on icy and snowy roads, an improved rapidly-exploring random tree (RRT) path planning algorithm is proposed. Firstly, a dynamic model introducing road adhesion coefficient on icy and snowy roads is established. Secondly, the global target deflection sampling combined with the front pointing and steering angle of the vehicle, combined with the collision avoidance detection and the maximum curvature constraint under the velocity-adhesion coefficient, is used to improve the traditional RRT algorithm problem.Finally, a double quintic polynomial is used for path smoothing to ensure stability, brake constraints, and comfort. The performance of the improved algorithm RRT is compared with that of the traditional algorithm under multi-scenario conditions through the joint simulation of MATLAB-Simulink and CarSim. The experiments show that the improved RRT algorithm significantly improves the path smoothness, reduces the curvature mutation, has short time, high success rate and good stability when driving on ice and snow.

Key words: autonomous vehicle, path planning, rapidly-exploring random tree, ice and snow roads, multi-scenario

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